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Found 4 papers, 2 papers with code
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
Our helper framework offers the algorithm designer high flexibility for constructing and analyzing the stochastic Cubic Newton methods, allowing arbitrary size batches, and the use of noisy and possibly biased estimates of the gradients and Hessians, incorporating both the variance reduction and the lazy Hessian updates.
Second-order optimization with lazy Hessians
This provably improves the total arithmetical complexity of second-order algorithms by a factor $\sqrt{d}$.
Optimization with Access to Auxiliary Information
We investigate the fundamental optimization question of minimizing a target function $f$, whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h$ whose gradients are cheap or more available.
Linear Speedup in Personalized Collaborative Learning
Collaborative training can improve the accuracy of a model for a user by trading off the model's bias (introduced by using data from other users who are potentially different) against its variance (due to the limited amount of data on any single user).
